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NONPARAMETRIC EULER EQUATION IDENTIFICATION AND ESTIMATION

Juan Carlos Escanciano (), Stefan Hoderlein, Arthur Lewbel (), Oliver Linton () and Sorawoot Srisuma

Econometric Theory, 2021, vol. 37, issue 5, 851-891

Abstract: We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption-based asset pricing Euler equations. Ours is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness). We also propose a novel nonparametric estimator based on our identification analysis, which combines standard kernel estimation with the computation of a matrix eigenvector problem. Our estimator avoids the ill-posed inverse issues associated with nonparametric instrumental variables estimators. We derive limiting distributions for our estimator and for relevant associated functionals. A Monte Carlo experiment shows a satisfactory finite sample performance for our estimators.

Date: 2021
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Related works:
Working Paper: Nonparametric Euler Equation Identification and Estimation (2020) Downloads
Working Paper: Nonparametric Euler Equation Identi?cation and Estimation (2020) Downloads
Working Paper: Nonparametric Euler Equation Identification andEstimation (2015) Downloads
Working Paper: Nonparametric Euler equation identification and estimation (2015) Downloads
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